scipy stats.betaprime() | Python Last Updated : 20 Mar, 2019 Comments Improve Suggest changes Like Article Like Report scipy.stats.betaprime() is an beta prime continuous random variable that is defined with a standard format and some shape parameters to complete its specification. Parameters : q : lower and upper tail probability a, b : shape parameters x : quantiles loc : [optional]location parameter. Default = 0 scale : [optional]scale parameter. Default = 1 size : [tuple of ints, optional] shape or random variates. moments : [optional] composed of letters [‘mvsk’]; 'm' = mean, 'v' = variance, 's' = Fisher's skew and 'k' = Fisher's kurtosis. (default = 'mv'). Results : beta prime continuous random variable Code #1 : Creating betaprime continuous random variable Python3 # importing scipy from scipy.stats import betaprime numargs = betaprimeprime.numargs [a, b] = [0.6, ] * numargs rv = betaprimeprime(a, b) print ("RV : \n", rv) Output : RV : <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000029482FCC438> Code #2 : betaprime random variates and probability distribution. Python3 1== import numpy as np quantile = np.arange (0.01, 1, 0.1) # Random Variates R = betaprime.rvs(a, b, scale = 2, size = 10) print ("Random Variates : \n", R) # PDF R = betaprime.pdf(quantile, a, b, loc = 0, scale = 1) print ("\nProbability Distribution : \n", R) Output : Random Variates : [ 1.59603917 1.92408727 1.2120992 0.34064091 2.68681773 22.99956678 1.45523032 2.93360219 23.93717261 18.04203815] Probability Distribution : [2.58128122 0.8832351 0.61488062 0.47835546 0.39160163 0.33053737 0.28490363 0.24941484 0.22101038 0.1977718 ] Code #3 : Graphical Representation. Python3 import numpy as np import matplotlib.pyplot as plt distribution = np.linspace(0, np.minimum(rv.dist.b, 5)) print("Distribution : \n", distribution) plot = plt.plot(distribution, rv.pdf(distribution)) Output : Distribution : [0. 0.10204082 0.20408163 0.30612245 0.40816327 0.51020408 0.6122449 0.71428571 0.81632653 0.91836735 1.02040816 1.12244898 1.2244898 1.32653061 1.42857143 1.53061224 1.63265306 1.73469388 1.83673469 1.93877551 2.04081633 2.14285714 2.24489796 2.34693878 2.44897959 2.55102041 2.65306122 2.75510204 2.85714286 2.95918367 3.06122449 3.16326531 3.26530612 3.36734694 3.46938776 3.57142857 3.67346939 3.7755102 3.87755102 3.97959184 4.08163265 4.18367347 4.28571429 4.3877551 4.48979592 4.59183673 4.69387755 4.79591837 4.89795918 5. ] Code #4 : Varying Positional Arguments Python3 1== from scipy.stats import arcsine import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 1.0, 100) # Varying positional arguments y1 = betaprime.pdf(x, 2.75, 2.75) y2 = betaprime.pdf(x, 3.25, 3.25) plt.plot(x, y1, "*", x, y2, "r--") Output : Comment More infoAdvertise with us Next Article scipy stats.betaprime() | Python V vishal3096 Follow Improve Article Tags : Python Python-scipy Python scipy-stats-functions Practice Tags : python Similar Reads scipy stats.beta() | Python The scipy.stats.beta() is a beta continuous random variable that is defined with a standard format and some shape parameters to complete its specification. f(x,α,β)=(Γ(α+β)xα−1(1−x)β−1​)/Γ(α)Γ(β) where: α>0 and β>0β>0 are the shape parameters of the Beta distribution.Γ Gamma is the Gamma fu 2 min read scipy stats.burr() | Python scipy.stats.burr() is an burr continuous random variable that is defined with a standard format and some shape parameters to complete its specification. Parameters : q : lower and upper tail probability a, b : shape parameters x : quantiles loc : [optional] location parameter. Default = 0 scale : [o 2 min read sciPy stats.alpha() | Python scipy.stats.alpha() is an alpha continuous random variable that is defined with a standard format and some shape parameters to complete its specification. Parameters : q : lower and upper tail probability x : quantiles a : shape parameter loc : [optional] location parameter. Default = 0 scale : [opt 1 min read scipy stats.gamma() | Python scipy.stats.gamma() is an gamma continuous random variable that is defined with a standard format and some shape parameters to complete its specification. Parameters : -> q : lower and upper tail probability -> x : quantiles -> loc : [optional]location parameter. Default = 0 -> scale : [ 2 min read scipy.stats.expon() | Python scipy.stats.expon() is an exponential continuous random variable that is defined with a standard format and some shape parameters to complete its specification. Parameters : q : lower and upper tail probability x : quantiles loc : [optional] location parameter. Default = 0 scale : [optional] scale p 2 min read Like